There are at least two challenges with quality management of service-oriented architecture based web service systems: 1) how to link its technical capabilities with customer's needs explicitly to satisfy customers' functional and nonfunctional requirements; and 2) how to determine targets of web service design attributes. Currently, the first issue is not addressed and the second one is dealt with subjectively. Quality Function Deployment (QFD), a quality management system, has found its success in improving quality of complex products although it has not been used for developing web service systems. In this paper, we analyze requirements for web services and their design attributes, and apply the QFD for developing web service systems by linking quality of service requirements to web service design attributes. A new method for technical target setting in QFD, based on an artificial neural network, is also presented. Compared with the conventional methods for technical target setting in QFD, such as benchmarking and the linear regression method, which fail to incorporate nonlinear relationships between design attributes and quality of service requirements, it sets up technical targets consistent with relationships between quality of web service requirements and design attributes, no matter whether they are linear or nonlinear.
L. Zhu and X. F. Liu, "Technical Target Setting in QFD for Web Service Systems using an Artificial Neural Network," IEEE Transactions on Services Computing, Institute of Electrical and Electronics Engineers (IEEE), Oct 2010.
The definitive version is available at https://doi.org/10.1109/TSC.2010.45
Keywords and Phrases
Bayesian Regularized Neural Network; QFD; Service Quality Management; Technical Targets Setting; Web Service System; Quality function deployment
Article - Journal
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